Big Data Systems • Log Aggregation & AnalysisEasy⏱️ ~2 min
What is Log Aggregation and Why Do We Need It?
Log aggregation is the practice of collecting telemetry data from distributed producers (applications, hosts, network devices, managed services) into a centralized pipeline that can ingest, process, store, and query data at massive scale. Instead of SSH-ing into individual servers to tail logs, you stream everything to a unified system that makes data searchable within seconds.
The architecture separates two critical paths. The hot path handles real time ingestion, indexing, and alerting with target P95 latency of 5 to 30 seconds from log emission to searchability. The cold path provides cheap long term retention and batch analytics on historical data. For example, Netflix processes trillions of events per day with seconds level end to end latency, while AWS CloudWatch Logs ingests trillions of events daily across multi tenant regions.
Production systems at scale see intense throughput. A typical large SaaS with 10,000 hosts emitting 0.5 to 2.0 MB/s each generates 5 to 20 GB/s aggregate ingest, which translates to 432 to 1,728 TB per day of raw data. After compression (typically 3x to 6x), storing 7 days hot requires 50 to 400 TB, while full text indexing all fields can push storage to 100 to 800 TB.
The alternative is chaos during incidents. Without centralized logs, debugging distributed systems means correlating timestamps across dozens of services manually, missing critical errors buried in verbose output, and having no way to alert on patterns across your fleet. Log aggregation transforms reactive fire fighting into proactive monitoring with sub minute detection of anomalies across millions of log lines.
💡 Key Takeaways
•Hot path targets P95 ingestion to search latency of 5 to 30 seconds and P95 query latency under 2 to 5 seconds for recent data, enabling real time incident response
•Production scale example: 10,000 hosts at 0.5 to 2.0 MB/s each generates 5 to 20 GB/s ingest or 432 to 1,728 TB per day raw, requiring 50 to 400 TB hot storage for 7 days after compression
•Netflix processes trillions of events per day with seconds level latency using decoupled pipeline with durable message bus, in stream enrichment, and tiered storage
•AWS CloudWatch Logs ingests trillions of events daily across multi tenant regions with seconds availability, demonstrating hot indexes for days plus archival for months
•Without centralized aggregation, debugging means manually correlating timestamps across services, missing critical patterns, and no fleet wide alerting capability
📌 Examples
Uber operates Jaeger at over 10 billion spans per day using Kafka centric telemetry backbone with multi cluster setup, Flink/Spark enrichment, and sub 30 second availability SLOs for search across hundreds of microservices
Oracle Cloud Logging provides multi tenant regional service with strong isolation, KMS encrypted storage, and IAM guarded routing for regulated workloads, routing to object storage for long term retention